Recent Advances in Wireless Communications and Networks Part 13 pot

From a practical aspect it could be also defined as the probability of a user receiving a network busy signal in a telephone service and can be measured using the following equation: Num

probability of a call being blocked (BP) or delayed more than a specified interval From a

practical aspect it could be also defined as the probability of a user receiving a network busy

signal in a telephone service and can be measured using the following equation:

Number of lost calls GoS BP

3.1 Bandwidth reservation-based CAC mechanism (BR CAC)

Our first proposed admission control mechanism is based on the bandwidth reservation

concept and is executed under “busy hour” conditions Under these conditions (i.e for high

arrival rate of VoIP calls), once a connection request arrives at the system, it is mapped onto

the corresponding service class Three main service classes are considered in our scheme:

i) the voice GBR ii) the non-voice GBR and iii) the non-GBR traffic types The two first

classes are included in the GBR family, while the third includes the connections that do not

require any Guaranteed Bit Rate In case of voice connections, the request is accepted if the

total available bandwidth (BW T) suffices to serve the incoming connection On the other

hand, restricted bandwidth (BW T - BW R) is provided to the other GBR classes, as the

algorithm’s aim is to prioritize VoIP calls over other types of connections In order to deal

with the connections that do not require any QoS guarantees (non-GBR), the requests are

always admitted, but no bandwidth allocation is considered The portion of the reserved

bandwidth for voice traffic is dynamically changed according to the traffic intensity of the

In the above expression, the traffic intensity ρ 1 is a measure of the average occupancy of the

base station during a specified period of time It is denoted as ρ = λ 1 / μ 1 , where λ 1 is the

mean arrival time for VoIP connections and μ 1 represents their mean service rate (duration)

Furthermore, BW 1 is the bandwidth needed for each VoIP call, while β∈ 0,1 denotes the [ ]

bandwidth reservation factor

Formula (2) implies that traffic intensity has an impact on the blocking probabilities of both

voice and non-voice connections It makes sense that applying this bandwidth reservation

scheme, the blocking probability for the VoIP connections is decreased, since a portion of

bandwidth is exclusively dedicated to this service type On the contrary, the available

bandwidth for the connections of the other service types is decreased and consequently the

blocking probability for the specific types increases

In bandwidth reservation schemes, one of the main difficulties is to avoid the inefficient

utilization of system resources However, in our case, the daily traffic variation establishes

the ability to predict an increase in VoIP calls, thus enabling us to tackle this problem

Therefore, our scheme outperforms classic bandwidth reservation mechanisms

3.1.1 Analytical model

In this section an analytical model for the proposed bandwidth reservation scheme is

developed, to derive the blocking probabilities for the different class types The results are

further verified by extensive simulations, presented in the following section

In order to simplify the analysis, the non-voice connections (e.g video, data etc) are treated

as a single class type with the same characteristics (i.e arrival rate, bandwidth demand) In

priorities Furthermore, non-GBR connections are not included in the model as they are

always accepted without any QoS guarantees

r,s-1 r-1,s r,s

r ⋅ μ

(s + ⋅ 1)μ 2

Fig 3 The two-dimensional Markov model's state transition diagram

Thus, the 2-dimensional continuous Markov model (Fig 3) can be used to analyze the

performance of the proposed scheme The state space of this Markov model is

( )

= , 0≤ ≤ ,0≤ ≤ , ⋅ 1+ ⋅ 2≤ T

BW The number of VoIP and non-VoIP connections

is represented by r and s, respectively Additionally, BW T and BW R represent the overall

and the reserved bandwidth, while BW and 1 BW represent the bandwidth that is needed 2

in order to serve each VoIP and non-VoIP connection, respectively We also define other

parameters as follows:

λ1 Arrival rate of VoIP connections

λ2 Arrival rate of non-VoIP connections

μ1

1 Service time for VoIP connections

μ2

1 Service time for non-VoIP connections

The state transmission diagram of the Markov model is shown in Fig 3

Its steady state equation is the following:

where p denotes the steady state probability of the system lying in the state r s, ( )r s and ,

φr s, , θr s, denote characteristic functions:

⎩

,

1, ( , )0,

r s

r BW s BW BW BW

The above functions are used in order to prevent a transition into an invalid state, according

to the previously defined restrictions Furthermore, considering the normalization condition

p , the steady state probability for each possible state can be obtained

The blocking probabilities for VoIP and non-VoIP connections are given by:

In order to clarify the mathematical analysis above, we provide two possible states of the

system’s Markov Chain Fig 4 depicts the exact form of the chain in each of the two cases

The first represents the state where there is no available bandwidth for non-voice

connections, hence not permitting the transition from s to s+1 On the other hand, the

second represents an equivalent situation along with the assumption that only voice

connections are served in the system (s=0), thus not allowing the transition from s to s-1

and vice versa

r ⋅ μ

(s + ⋅ 1)μ 2

Fig 4 Two examples of possible states of the system

First case: We assume that the system lies in the state (r, s), subject to the following

Under these assumptions and using the definitions of φr s, and θr s, , we derive the steady

state equation for the specific case:

Considering again the definitions of φr s, and θr s, , we derive the respective steady state

equation for this case, that is:

3.2 Dynamic call admission control algorithm (DCAC)

In the same context, we propose a second CAC algorithm that gives priority to the VoIP

calls during the “busy hour” In this scheme, unlike the previous one, no bandwidth

reservation takes place, while there is an effort towards a fairer handling of all connections

According to this CAC scheme, the eNB accepts all the VoIP flows if the available

bandwidth suffices in order for the calls to be served In the case of non-VoIP flows there is

an outage probability that depends both on the arrival rate of VoIP requests as well as on

the available bandwidth The requests of non-GBR connections are always admitted, but no

bandwidth allocation is considered, since non-GBR flows do not need any QoS guarantees

The proposed algorithm has two main parameters: the arrival rate of VoIP requests and the

available bandwidth of the system The outage probability for the non-VoIP connections

increases either when the arrival rate of the VoIP calls grows or when the available

bandwidth decreases The capacity required in order to serve all the upstream connections

can be approximated with the following expression:

All the parameters in the above expression have been already defined However, it should

be stressed that the index i corresponds to different service types and can take values 1 and

2 for VoIP and non-VoIP traffic, respectively

In case that the system bandwidth suffices to serve the flows of all service types, the outage

probability is equal to zero Due to this fact, the proposed admission control has the same

output as classic admission control schemes under light traffic conditions in the network

On the contrary, in overloaded environments where the bandwidth is not sufficient for all

connections, an admission control algorithm is required in order to provide different levels

of priority to the various connections

Let us consider the arrival rate of the VoIP requests, defined as λ 1 If this rate is higher than a

specific threshold there will be an outage probability for the requests of the other GBR

service types This threshold is defined by the administrator/operator of the network, by

considering the network parameters, e.g the arrival rate of VoIP calls during “busy hour”

The value of the outage probability fluctuates between Poutmin and Poutmax, depending on

the available system bandwidth In the extreme case that we have no available bandwidth,

the overall outage probability becomes Poutmax Adversely, when the total bandwidth of the

system is available and no connections are being served, i.e., BWavailable/BW T = 1, the outage

probability becomes Poutmin, since there is enough bandwidth in order for the connections of

all types to be served These borderline values are selected by the system’s operator

according to each traffic class’ desired level of priority On the other hand, whenever the

arrival rate of VoIP connections is smaller than this arrival rate threshold, we assume that

we are out of “busy hour” and, therefore, the outage probability equals zero

The flowchart in Fig 5 depicts the connection acceptance/rejection procedure in the

proposed Dynamic Connection Admission Control (DCAC) algorithm The basic process of

the connection request flow has been described above In the last part of the algorithm, there

is an estimation of the available bandwidth ratio in order to derive the exact value of the

outage probability (the higher the ratio, the lower the probability) In particular, the Poutmin

is a system parameter, designated by the operator, which determines the desirable level of

priority to be assigned to the voice calls By adding this value to the normalized bandwidth

ratio, the outage probability for the specific connection is derived

Accept Connection

Fig 5 Dynamic connection admission control (flowchart)

4 Performance evaluation

In order to evaluate the performance of the proposed CAC schemes and verify the validity

of the analytical formulation, corresponding event-driven C++ simulators that execute the

rules of the algorithms have been developed In this section, the simulation set up is

described, followed by a discussion of the obtained results

4.1 Simulation scenario

Based on the physical capabilities of the LTE technology, we assume that the overall

bandwidth for the uplink traffic is 4 Mb/s Assuming that the non-VoIP traffic consists

mainly of audio and video data, an average bandwidth of 128 kb/s for each connection is

considered (Koenen, 2000) The codec chosen to generate VoIP traffic is the G.711, resulting

to a constant bit rate of 64 kb/s Each result was produced by running the simulation 100

times using different seeds, while we simulate 3600 seconds of real time in order to be in

accordance with the definition of “busy hour”

In order to evaluate the efficiency of the proposed algorithms, a research on the

state-of-the-art admission control mechanisms for the LTE standard has been conducted Several

schemes in the literature accept a new connection when the following condition is satisfied:

where C reserved represents the capacity reserved by the already admitted connections in the

system, TR service i denotes the traffic rate that should be guaranteed to the new connection i

of service type service and C total is the total available capacity

We refer to these methods as capacity-based (CB) algorithms in order to distinguish from

our proposed algorithms which are either based on the bandwidth reservation (BR) concept

or follow a dynamic approach (DCAC) In order to study the performance of our mechanisms we have carried out simulation tests by varying the VoIP requests arrival rate, thus providing a large range of voice traffic that fluctuates between 15 and 240 connections/min However, it should be clarified that the rate request of the voice connections remains constant during the busy hour The system parameters that are presented in Table 1, define that the arrival rate of all connections follows a Poisson distribution, while the mean service time for the connections is exponentially distributed

Table 1 System parameters

Under these assumptions and considering λ 1 = 1 connection/s, the system can serve about 98% of the VoIP calls if all the requests of the other classes are rejected, which means that the network is overloaded Furthermore, in the specific case we use a single admission control based on bandwidth availability (CB) where all the requests are accepted if there is enough bandwidth to serve them, regardless of the class that they belong to, the system serves about 57% of the VoIP flows and 34% of the other flows

Finally, before proceeding to the simulation results, let us recall that the aim of the proposed schemes is to serve more voice traffic by reducing the GoS, and consequently the blocking probability, of VoIP calls

4.2 Performance results

Simulation results are compared to those obtained with the mathematical model presented

in section 3.1.1 First, it can be observed that the simulation results verify the mathematical analysis, with the difference varying in a range of less than 2% (Fig 6) Comparing the first proposed admission control to traditional schemes for different values of arrival rates for the VoIP connections, we observe that the BR CAC outperforms single admission control methods in terms of GoS, without any deterioration in the overall system performance Fig

6 depicts the GoS among various arrival rates of VoIP calls It is observed that, using our proposed CAC, a better system performance in terms of voice communication is achieved,

as there is a significant enhancement in GoS (10-40%) of VoIP traffic

On the other hand, the GoS of the other types of connections is increased as expected, but examining the system considering the total number of connection requests (both VoIP and non-VoIP) we achieve a more efficient utilization of system resources as we observe an enhancement in the total GoS ratio for high arrival rates of VoIP connections (i.e rates greater than 1 connection/s)

Fig 6 GoS vs VoIP Calls Arrival Rate (proposed Bandwidth Reservation (BR) CAC vs Capacity-based (CB) CAC including analytical results)

Fig 7 GoS vs VoIP Calls Arrival Rate (proposed DCAC vs Capacity-based (CB) CAC)

The simulation results of the proposed Dynamic Call Admission Control (DCAC) algorithm comparing to the Capacity-based (CB) algorithm are presented in Fig 7 This algorithm not only improves the voice traffic service, but also enhances the overall system performance However, in this case the level of prioritization of the VoIP calls over the other type of traffic

is lower compared to the bandwidth reservation scenario, thus resulting in a fairer distribution of the system resources

Furthermore, it is interesting to observe that even for the lower arrival rates of VoIP calls (i.e 0.25 and 0.5 calls/s) the DCAC handles efficiently the system’s bandwidth, due to its flexibility, while the BR scheme fails to overcome the Capacity-based algorithm The comparison between the two proposed schemes is given in Fig 8 In this figure, even if there

is no further information provided, it can be clearly seen how the two proposed schemes deal with the different types of traffic, as well as their overall performance An interesting observation is that, in this particular scenario, the curves for the total GoS for the two schemes cross when the arrival rate is approximately 1.3 connections/s Below this threshold (i.e for relatively low traffic conditions) the DCAC outperforms the proposed BR scheme, while above this threshold (i.e for relatively high traffic conditions) the BR scheme handles the total connections in a more efficient way

The system’s bandwidth is a main parameter of the DCAC In Fig 9 the provided Grade of Service for various values of bandwidth is plotted As far as networks with restricted bandwidth capabilities are considered, we observe that our proposed dynamic admission control algorithm outperforms single methods, as it improves the GoS of both VoIP calls (11-27%) and of the total number of connections (8-10%) as well

Fig 8 GoS vs VoIP Calls Arrival Rate (proposed Bandwidth Reservation (BR) CAC vs proposed DCAC)

Fig 9 GoS vs Total System’s Bandwidth (proposed DCAC vs Capacity-based (CB) CAC)

5 Conclusion

In this chapter, two new admission control schemes for the LTE architecture have been presented The first mechanism (BR CAC) is based on bandwidth reservation concept, while the second (DCAC) reacts dynamically, depending on the available system’s bandwidth Compared to simple, Capacity-based (CB) admission control methods for 4G networks, the proposed solutions improve the Grade of Service of the voice traffic, without deteriorating the total system performance The main idea of the proposed schemes is that the base station serves more VoIP calls by considering the “busy hour” phenomenon Finally, although both the proposed algorithms have been designed with LTE infrastructure in mind, the flexibility

of the schemes enables their adaptation to other similar technologies such as IEEE 802.16 (WiMAX)

6 Acknowledgment

This work has been funded by the Research Projects GREENET (PITN-GA-2010-264759), CO2GREEN (TEC2010-20823) and CENTENO (TEC2008-06817-C02-02)

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A Semantics-Based Mobile Web Content Transcoding Framework

programs, real-time processing capability, and rendering functionality These ad hoc

limitations have become barriers in human-computer interaction Most web contents, such

as web pages and images, are mainly in the HTML format, which is designed for desktop computers Without proper modification, most rendered web contents in hand-held devices encounter distorted or fragmented user interface, broken images, slow responses, etc These

ad hoc characteristics of hand-held devices have become a barrier in enhancing web availability

In this chapter, we illustrate how a semantics-based content adaptation framework could be utilized to fill up the computational gap between mobile devices and desktop computers The transcoding mechanism of our framework, called Content Adaptation Proxy Server [CAPS], resides behind web servers In CAPS, web pages and image files are transcoded according to: (1) RDF (Resource Description Framework) (Manola et al., 2004) of web content; and (2) semantics extracted from the CC/PP (Composite Capability Preferences Profiles) (Klyne et al., 2004) client device configuration These semantic properties will be stored and interpreted inside the Jena Inference System (Carroll et al., 2004) as knowledge facts to obtain proper transcoding parameters for each particular device Then web pages with proper layout modification and images with proper rendering parameters for each particular device will be constructed and delivered This technology will recreate contents suitable for resource-limited devices to balance information loss and information availability

For the rest of this chapter, we will review related work in Section 2 In Section 3, we describe the design principle and system architecture of CAPS Semantics extraction and knowledge base construction for the Jena Inference System are discussed in Sections 4 and 5

devices Section 8 concludes this paper

2 Related work

According how many web content types are handled, web content adaptation could be classified into: universal and specific web content adaptation Universal web content adaptations are mostly proxy-based and could be applied to web pages and various multimedia types Their functions include integrating various web content types for the rendering Section 2.1 will cover proxy-based universal web content adaptation Specific web content adaptation focuses on the algorithm design The most frequently studied web content type is HTML files Due to the complexity in analyzing HTML files, proper adaptation of HTML files in mobile devices is very difficult Section 2.2 will cover works in using heuristics to adjust layouts of HTML documents to fit a particular mobile device We will cover how the semantic web technology has been applied to web content adaptation in Section 2.3

2.1 Proxy-based universal web content transcoding

Nagao et al (2001) proposed constructing on the Web a system framework using XML and external annotations to Web documents They proposed three approaches for annotating documents—linguistic, commentary, and multimedia With annotated documents that computers can understand and process more easily, their framework allowed content to reach a wider audience with minimal overhead

Lum and Lau (2002) built a quality-of-service oriented decision engine for content adaptation They designed flows for content negotiation and processing for multimedia contents

Ardon et al (2003) prototyped a proxy-based web transcoding system based on network access control, user preferences, and displaying capability of equipments Since all transcoding procedures were finished in the content provider’s server, this server-centric framework avoided potential copyright problems

Sacramento et al (2004) designed the Mobile Collaboration Architecture [MoCA], a middleware for developing and deploying context-aware collaborative applications for mobile users It comprises client and server APIs, core services for monitoring and inferring the mobile devices' context, and an object-oriented framework for instantiating customized application proxies

Hua et al (2006) integrated content adaptation algorithm and content caching strategy for serving dynamic web content in a mobile computing environment They constructed a testbed to investigate the effectiveness of their design in improving web content readability

on small displays, decreasing mobile browsing latency, and reducing wireless bandwidth consumption

Hsiao et al (2008) proposed the architecture of versatile transcoding proxy (VTP) The VTP architecture can accept and execute the transcoding preference script provided by the client

or the server to transform the corresponding data or protocol according to the user's specification They adopted the concept of dynamic cache categories and proposed a new replacement algorithm for caches

Nimmagadda et al (2010) presented a content adaptation method for multimedia presentations constituting media files with different start times and durations They performed adaptation based on preferences and temporal constraints specified by authors and generate an order of importance among media files Their method can automatically generate layouts by computing the locations, start times, and durations of the media files

2.2 Web page transcoding

Bickmore and Girgensohn (1999) designed a “Digestor System” which was capable of automatic filtering and re-authoring so that WAP-enabled cellular phones could read HTML contents Their basic idea was to extract plain texts in the HTML document by discarding all formatting elements and unnecessary information The result was then divided into a navigation page and several plain text sub-pages They also utilized transcoding cache to diminish the run-time overhead

Huang and Sundaresan (2000) tried the semantics approach in transcoding web pages to improve web accessibility for users Their system was designed to improve the interface of e-business transactions and to extend interoperable web forms to mobile devices They used XML/DTD to specify the semantic and grammatical relationship among web contents, so that web forms could achieve consistency, simplicity and adaptability The advantage of this system was its ability to provide concept-oriented content adaptation, but it was difficult to

be extended

Buyukkokten et al (2001) used an “accordion summarization” transcoding strategy where

an HTML page could be expanded or shrunk like an accordion The HTML page was restructured as a tree according to the semantic relationships among its textual sections All textual sections were split into several Semantic Textual Units, which were automatically summarized Users could check each summary to expand the node for detailed information However, this framework only worked in the browser they designed for digital libraries Hwang et al (2003) also treated web page layout as a tree according to the tag hierarchy They defined a grouping function to transform such a tree into sub-trees, and introduced a filtering mechanism to modify the sub-trees for adequate display in the target device They analyzed specific web page layout structure and re-authored, according to heuristics, web pages for several mobile devices Each of their transcoding method could handle only specified layout structures of web pages and did not consider mobile device characteristics

2.3 Semantic web technologies in web content transcoding

In addition to Huang and Sundaresan (2000), several researchers have tried to incorporate semantic web technologies into web content transcoding DELI (Butler, 2002), an HP Semantic Lab project, adopted simple negotiation algorithms for rewriting web pages based

on context information, like user preference and device capabilities Due to lack of implementation, its applications were restricted

Hori et al (2000) proposed an annotation-based system for Web content transcoding They introduced a framework of external annotation, in which existing Web documents were associated with content adaptation hints as separate annotation files This annotation-based transcoding system was then extended with particular focus on the authoring-time integration between a WYSIWYG annotation tool and a transcoding module

Glover and Davies (2005) used heuristic algorithms to find proper pre-defined web page templates according to device attributes Their focus was in applying XML/XSLT styles to database contents retrieved in dynamic web pages